用图注意力网络和对比学习构建脑-认知指纹，精准刻画个体差异。
Brain-Cognition Fingerprinting via Graph-GCCA with Contrastive Learning
- 基于图注意力与广义典型相关分析建模脑与认知关系。
- 在多时点数据上显著提升性别和年龄识别准确率。
- 生成可解释的跨模态交互，适合神经科学研究者使用。
许多纵向神经影像研究旨在通过分析脑功能与认知之间的动态交互，深化对大脑老化及疾病的理解。这需要在考虑个体随时间变化差异的前提下，准确编码二者间的多维关系。为此，我们提出一种无监督学习模型（称为基于对比学习的图广义典型相关分析，CoGraCa），通过图注意力网络和广义典型相关分析编码脑-认知关系，并利用个体化、多模态对比学习构建反映每个人独特神经与认知表型的脑-认知指纹。将CoGraCa应用于包含多个访问时间点的健康人群纵向数据集（含静息态功能性MRI和认知测量），结果表明生成的指纹有效捕捉显著个体差异，在识别性别和年龄方面优于当前单模态及基于CCA的多模态模型。更重要的是，该编码方法提供了两模态间可解释的交互关系。
原文摘要 · Abstract (English)
Many longitudinal neuroimaging studies aim to improve the understanding of brain aging and diseases by studying the dynamic interactions between brain function and cognition. Doing so requires accurate encoding of their multidimensional relationship while accounting for individual variability over time. For this purpose, we propose an unsupervised learning model (called \underline{\textbf{Co}}ntrastive Learning-based \underline{\textbf{Gra}}ph Generalized \underline{\textbf{Ca}}nonical Correlation Analysis (CoGraCa)) that encodes their relationship via Graph Attention Networks and generalized Canonical Correlational Analysis. To create brain-cognition fingerprints reflecting unique neural and cognitive phenotype of each person, the model also relies on individualized and multimodal contrastive learning. We apply CoGraCa to longitudinal dataset of healthy individuals consisting of resting-state functional MRI and cognitive measures acquired at multiple visits for each participant. The generated fingerprints effectively capture significant individual differences and outperform current single-modal and CCA-based multimodal models in identifying sex and age. More importantly, our encoding provides interpretable interactions between those two modalities.
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